R/datasets.R

In KODAMA: Knowledge Discovery by Accuracy Maximization

# This function creates a data set based upon data points distribuited on a Ulisse Dini's surface.
dinisurface = function (N = 1000) 
{
  u = sort(runif(N) * 4 * pi)
  v = runif(N)
  a = 1
  b = 0.2
  x = a * cos(u) * sin(v)
  y = a * sin(u) * sin(v)
  z = a * (cos(v) + log(tan(v/2))) + b * u
  data = cbind(x, y, z)
  return(data)
}



# This function creates a data set based upon data points distribuited on a Helicoid surface.
helicoid = function (N = 1000) 
{
  a = 1
  p = sample((seq(1, -1, length.out = N)))
  t = seq(-pi, pi, length.out = N)
  x = p * cos(a * t)
  y = p * sin(a * t)
  z = t
  data = cbind(x, y, z)
  return(data)
}


# Produces a data set of spiral clusters.

spirals = function (n = c(100, 100, 100), sd = c(0, 0, 0)) 
{
  clusters = length(n)
  x = NULL
  y = NULL
  for (i in 1:clusters) {
    t = seq(1/(4 * pi), 1, length.out = n[i])^0.5 * 2 * pi
    a = rnorm(n[i], sd = sd[i])
    x = c(x, cos(t + (2 * pi * i)/clusters) * (t + a))
    y = c(y, sin(t + (2 * pi * i)/clusters) * (t + a))
  }
  cbind(x, y)
}



# Computes the Swiss Roll data set of a given number of data points.

swissroll = function (N = 1000) 
{
  n <- 3
  m <- 2
  tt <- sort((3 * pi/2) * (1 + 2 * runif(N)))
  height <- 21 * runif(N)
  x <- tt * cos(tt)
  y <- height
  z <- tt * sin(tt)
  data = cbind(x, y, z)
  return(data)
}